Which type of model is commonly used for sentiment analysis?

A simple neural network that classifies tags as positive or negative is commonly used for sentiment analysis because this task involves determining the emotional tone behind a body of text. Sentiment analysis typically categorizes texts into sentiments such as positive, negative, or neutral, and using a neural network for this purpose allows the model to learn patterns from labeled training data.

Neural networks can effectively capture the nuances of language and are well-suited for classification tasks, which makes them ideal for sentiment analysis applications. The model takes in text as input and produces an output classification, which aligns with the objective of sentiment analysis: to classify and understand emotions expressed in written content.

In contrast, other models mentioned in the other choices serve different purposes. Convolutional neural networks are primarily designed for processing grid-like data, such as images, and are less appropriate for purely text-based sentiment tasks. Regression models are aimed at predicting continuous numerical outcomes, making them unsuitable for categorical sentiment classification. Lastly, rule-based systems rely on predefined rules rather than learning from data, which limits their flexibility and adaptability in the context of dynamic language use often encountered in sentiment analysis. Thus, a simple neural network is the most relevant and effective choice for this task.